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Core
Agent Security: Tool Poisoning, Memory Poisoning, Containment
Agent security covers threats that only exist once an LLM can call tools and act on their results: malicious tool or MCP responses, poisoned long-term memory, privilege escalation through tool misuse, and goal hijacking. The defense is runtime containment, least-privilege tools, kill-switches, and blast-radius limits, not better prompting. Applied AI interviews probe it because anyone shipping agents has to reason about what happens when an untrusted string steers a system that can spend money or delete data.
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RELATED CONCEPTS
PRACTICE THIS IN REAL QUESTIONS
AI Security, Privacy & GovernanceHow do tool-result and memory poisoning attacks compromise an AI agent, and how do you defend?→AI Security, Privacy & GovernanceYour agent calls tools on behalf of users. How do you design its identity, credentials, and authorization?→AI Security, Privacy & GovernanceA tool-using agent reads untrusted web content. How do you defend against prompt injection?→System Design for AI in ProductionDesign a text-to-SQL feature: let users ask questions in natural language over a real database.→RAG & Agent System DesignWhat is the Model Context Protocol (MCP), and how do you design good tools for an agent?→System Design for AI in ProductionDesign a fraud-detection system that uses LLMs (beyond a classic ML classifier).→
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